Field of the Invention
Embodiments of the invention relate to hydrocarbon industries and, more particularly, systems, methods, and non-transitory computer-readable medium having computer program stored therein for borehole image analysis.
Description of the Related Art
An understanding of the subsurface material within a hydrocarbon reservoir may play an important role in development of the hydrocarbon reservoir to produce hydrocarbons, such as oil and gas. More specifically, an ability to identify types and their associated properties—sometimes called “lithotype” or, in some circumstances “facies”—of subsurface material may significantly enhance development of the hydrocarbon reservoir. At least one reason that such an understanding may be important is that the types and properties of subsurface material, as well as the orientation and position of different types of material, may affect how hydrocarbons flow through the reservoir. For example, an individual material type may be associated with porosity and permeability ranges that differ greatly from those of another material type. An ability to identify the material types and characteristics within a hydrocarbon reservoir may consequently enhance models of the hydrocarbon reservoir that are used to simulate fluid flow within the hydrocarbon reservoir, for example. Numerous approaches to identifying subsurface material types and properties may exist, including analysis of borehole images.
Borehole images—representations of walls of a hydrocarbon well borehole—may be analyzed to help identify subsurface material types and characteristics. Borehole images may be acquired, for example, by measuring material resistivity along uncased borehole walls. For example, one or more pads containing electrodes and sensors (“buttons”) may be positioned against a borehole wall. The one or more sensor pads may apply an electrical current into the borehole wall to measure the resistivity of the subsurface material surrounding the borehole. This procedure may be repeated with the one or more pads oriented in other directions within the borehole, as well as at a plurality of depths within the borehole. That is, measurements or readings of material resistivity may be taken at various depths along the length of a borehole, and they may further be taken in a plurality of directions at each depth. Consequently, material resistivity may be measured for a significant portion of the subsurface material surrounding the borehole. The measured resistivity data may then be used to develop a “picture” of the subsurface material through which the borehole passes. As a result, borehole image (BHI) data—sometimes called borehole image log (BHI) data or simply BHI—may include resistivity measurements from within a borehole.
Borehole image data may sometimes be analyzed in conjunction with other data, including, for example, openhole log data (sometimes described as “openhole data,” “open hole log data,” “open hole data,” or “well logs”) and core data. Openhole log data may include measured data from within an uncased borehole such as, for example, density measurement data, neutron measurement data, gamma ray (GR) measurement data, induction log data, lateral log (“laterlog” or “laterolog”) data, porosity log data, photoelectric (PE) curve data, and petrophysical calculations (e.g., water saturation). Core data, on the other hand, may include data measured, observed, or derived from a core sample associated with a hydrocarbon well. A core sample can include, for example, a piece of subsurface material that has been removed from a hydrocarbon wellbore, at a substantially known depth, during or after drilling. A core sample may be analyzed to identify, for example, one or more types of subsurface material within the core sample, presence and location of pores and vugs within the core sample, and grain size of subsurface material within the core sample. As will be understood by those skilled in the art, grain size may include, for instance, particle size of individual components that make up subsurface material, such as, for example, clastic material. Although a core sample may be related to subsurface material properties only at one interval of the wellbore from which the core sample originated, information garnered from the core sample may be used—with openhole log data and borehole image data—to predict and model subsurface material at other depths, i.e., at uncored intervals of the wellbore. For example, subsurface material type, orientation, fabric, and texture may be modeled. Texture may include, for example, the size, shape, and arrangement of grains and spaces between grains of subsurface material, such as rock, as will be understood by those skilled in the art. Furthermore, fabric may include the pervasive internal structure and arrangement of subsurface material, such as the preferred orientations of grains or fragments, related to the primary depositional or secondary diagenetic processes. A model of subsurface material surrounding a wellbore may itself be used to enhance a model of the hydrocarbon reservoir associated with the wellbore.
Also, for example, as described in U.S. Patent Application Publication No. 2012/0221306 (Hurley et al.), core samples first may be described to identify facies, fabrics, and material types 271, for example, as depicted in FIG. 1a. Facies may include a body or unit of, typically, sedimentary rock or subsurface material with specified characteristics from which an inference may be drawn that the subsurface material was formed by a particular geological process or within a particular geological environment. Porosity and permeability of a core sample then may be analyzed 272. Existing core analyses, core descriptions, and material types then may be integrated 273 before well logs are analyzed 274. Finally, borehole images and openhole log data may be interpreted and compared to facies as identified from a core sample 275 as part of a “core calibration” process. That is, an interpreter may integrate grain size, lithology, and texture simultaneously on a well-by-well basis to produce electrofacies 264, given openhole log data sets 261, core data sets 262, and borehole image reading sets 263, as depicted, for example, in FIG. 1b. Electrofacies, as will be understood by those skilled in the art, may include facies that are determined from analysis of a diagnostics or individual set of wire-line log responses that characterize the physical properties of subsurface material and fluids contained within or related to a volume and depth of investigation by a wire-line logging tool. Electrofacies may usually be confirmed in the first instance by calibration to core data.
Manual interpretation of borehole image log (BHI) fabrics and textures, in conjunction with openhole logs and core calibration, is an established industry methodology for extrapolating into uncored intervals. This manual methodology, although often utilized, may usually only be detailed in internal company reports, e.g., in Ajay Samantray, Martin Kraaijveld, Waleed Bulushi & Laurent Spring, Interpretation and Application of Borehole Image Logs in a New Generation of Reservoir Models for a Cluster of Fields in Southern Oman, AAPG Memoir 92, Spring 2010, at 343, 343-57 (hereinafter Samantray). It may be a time-intensive method that relies heavily on the experience of the BHI interpreter for consistency. Typically, during manual interpretation when multiple wells and reservoir intervals are involved, spurious variations in reservoirs' rock properties from similar facies may be generated due to the introduction of minor inconsistencies in “lithotype” classes. Lithotype, as will be understood by those skilled in the art, may include subsurface material or a geological unit characterized on the basis of a combination of selected physical, textural, or stratigraphic parameters.
Manual interpretation of facies from borehole images has been applied to many reservoirs over the last twenty years. It is an established industry method for creating geologically-based facies utilizing borehole image logs (BHI) in conjunction with open hole logs and core calibration to extrapolate into uncored intervals. The methodology may rely heavily on the experience of the BHI interpreter to correctly identify the image features, textures, and orientation data. It is an established technique, but the methodology may largely be concealed and only occasionally elucidated through reference to specialist internal company reports or one-off papers, e.g., in Samantray. In Samantray, the borehole image log facies interpretation scheme by MacPherson et al., (2005) from a company report was reproduced. This scheme, now widely used, may be an amalgamation of previous works, but what may make it particularly unique, as displayed in Samantray, is its simplicity. On a single page it may summarize open hole logs with image log textures.
For example, an image facies scheme for clastic rocks after MacPherson may be illustrated, for example, in FIG. 1e(i). In FIG. 1e(i), an experienced geologist may have created a field-specific image facies scheme from a geological image interpretation of borehole image features with a petrophysical interpretation of open hole logs. Characteristic log cutoff values for certain rock types (e.g., argillaceous sandstones) may have been determined depending on formation log responses, log data availability, and data quality in the field. These image log rock facies types may be calibrated against core data in initial phases of projects and combined with geological fabrics interpreted from the image log and core, as illustrated, for example, in FIG. 1e(ii). That is, image facies for clastic rocks after MacPherson may be illustrated in FIG. 1e(ii), for example. This integration of geological textures identified from the borehole images, core, and the open hole logs may create a geological facies (e.g., bedded argillaceous sandstone). The method may rely heavily on the experience of the BHI interpreter to correctly identify the image features, texture, and orientation data. The developed facies may then be used to identify depositional environments and sediment dispersal directions within the geological reservoir models. Finally, the facies may be combined into facies associations to assess reservoir property groups rather than geological facies, as illustrated, for example, in FIG. 1e(iii). A facies association, as will be understood by those skilled in the art, may include a group of individual facies that are considered to have been in a particular sedimentological environment or by particular depositional processes. More specifically, an image facies association scheme for clastic rocks after MacPherson may be illustrated in FIG. 1e(iii), for example. This may help to distinguish between good and poor reservoirs for the simulation of the reservoir, e.g., a cross-bedded clean sandstone (Sx) is seen to have a better reservoir quality than a laminated clean sandstone.
The manual interpretation of borehole image log (BHI) fabrics and textures, in conjunction with open hole logs and core calibration, is an established industry methodology for extrapolating into uncored intervals. This manual methodology, although often utilized, may usually only be detailed in internal company reports, e.g., in Samantray. It was only recently fully reproduced by the inventors. It may be a time intensive method that relies heavily on the experience of the BHI interpreter for consistency. Typically, during manual interpretation when multiple wells and reservoir intervals are involved, spurious variations in reservoirs' rock properties from similar facies may be generated due to the introduction of minor inconsistencies in “lithotype” classes.
For example, a project utilized a borehole image log facies interpretation scheme developed by MacPherson et al., (2005) as reported in Samantray. This methodology may be frequently applied in the industry but seldom detailed outside of company reports. It may be only occasionally elucidated through reference to specialist internal company reports or one-off papers, as in Samantray. This scheme, now widely used, may be an amalgamation of previous works, but what makes it particularly unique, as displayed in Samantray, is its simplicity. On a single page it may summarize openhole logs with image log textures. For example, in FIG. 1c(i), an experienced geologist may have created a field-specific electrofacies scheme from a geological image interpretation of borehole image features with a petrophysical interpretation of openhole logs. That is, an example of an electrofacies scheme for clastic rocks after MacPherson in Samantray, for example, may be illustrated in FIG. 1c(i). An electrofacies scheme may include wireline response data 221 and borehole image response data 222, for example. As illustrated, wireline response data 221 (i.e., openlog data) may include measurements of gamma ray radioactivity in API 227, including a mean gamma ray measurement 223. Wireline response data 221 may also include measurements of density/neutron porosity 224, measurements of sand separation 225, and measurements of shale separation 226, as will be understood by those skilled in the art. Further, borehole image response data 222 (i.e., BHI data) may include indications of bedding types 228. Borehole image response data 222 may further include indications of often conductive mottled 229 and indications of conductivity of image character (in hydrocarbon leg) 230. Borehole image response data 222 may still further include indications of where a subsurface material falls on a scale between finely speckled image character 232 and flat/matte image character 231. An electrofacies scheme, as depicted, may include data related to heterolithics, such as an indication of how regular laminae are 233 and an indication of caliper enlargement 234 (i.e., increasing caliber size). Characteristic log cutoff values for certain rock types (e.g., argillaceous sandstones) may have been determined depending on formation log responses, log data availability, and data quality in the field. These image log rock facies types may be calibrated against core data in initial phases of projects and combined with geological fabrics interpreted from the image log and core, as illustrated in FIG. 1c(ii), for example. That is, an example of electrofacies for clastic rocks after MacPherson in Samantray, for example, may be illustrated in FIG. 1c(ii). For each of the image associations 235, electrofacies may include an image facies code 236, an image facies 237, and possible alternatives 238. This integration of geological textures identified from the borehole images, core, and the openhole logs may create a geological facies (e.g., bedded argillaceous sandstone). The method may rely heavily on the experience of the BHI interpreter to correctly identify the image features, texture, and orientation data. The developed facies may then be used to identify depositional environments and sediment dispersal directions within the geological reservoir models. For example, electrofacies (old workflow) used for a depositional environment and sediment dispersal analysis in Oman by MacPherson in Samantray is illustrated, for instance, in FIG. 1d. As depicted, recordings and observations may include (1) wireline caliper, GR, PE, density, and neutron porosity recordings and observations; (2) static and dynamic images recordings and observations; (3) manual picks, bedding, and fractures/faults recordings and observations; and (4) stratigraphy and relative porosity difference (RPD) recordings and observations. More specifically, wireline caliper, GR, PE, density, and neutron porosity recordings and observations may include hole shape measurements/representations 278, gamma ray (GR) and photoelectric (PE) measurements 279, density and neutron porosity measurements 280, and depth in meters 281. Static and dynamic images recordings and observations may include static normalized resistivity image data 282 and dynamic normalized resistivity image data 283. Additionally, manual picks, bedding, and fractures/faults recordings and observations may include manual dips 284, bedding azimuth frequency histograms by structural zone 285, and strike frequency histograms and poles by structural zone 286. Further, stratigraphy and RPD recordings and observations may include uncorrected fracture density measurements 287, structural dip and fault position measurements 288, stratigraphy 289, and SW, porosity, density, and RPD measurements 290. In addition to recordings and observations, electrofacies and sediment dispersal analysis may include interpretation, which in turn may include borehole image facies, facies association, and depositional sub-environment. More specifically, interpretation may include indications of image facies 291, indications of gross facies association 292, indications of depositional environment and cross bedding 293, and indications of gross depositional environment 294. Interpretation may also include residual dip azimuth histograms by gross depositional environment polar plots of low angle sandstones and heterolithics with residual dip greater than three degrees 295. Residual dip azimuth histograms 295 may in turn include indications of residual dip of cross beddings, sandstone bedding, and erosional surfaces 296. Sediment dispersal analysis, as will be understood by those skilled in the art, may be determined as an overall general direction of movement of grains that constitute facies. For example, different mechanical or biological mechanisms—e.g., wind (saltation), water (traction), and mass movement (land slips)—may physically move grains that make up a formation. But the sum of and recognition of the individual directions of movement from interpretation of image log fabrics are sediment dispersal and sediment transport trend analysis. Finally, the facies may be combined into facies associations to assess reservoir property groups rather than geological facies, as illustrated in FIG. 1c(iii), for example. An electrofacies association scheme for clastic rocks after MacPherson in Samantray may be depicted, for example, in FIG. 1c(iii). An electrofacies association scheme may include, for instance, image association names 239, dominant facies 240, subordinate facies 241, and common sub-environments of occurrence 242. As depicted, image facies codes are represented by different colors and patterns, although the colors or patterns may vary. This may help to distinguish between good and poor reservoirs for the simulation of the reservoir, e.g., a cross-bedded clean sandstone (Sx) is seen to have a better reservoir quality than a laminated clean sandstone.